{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data/eval_collection/Snowflake500+Routing+Prune100+Rerank20/testset-50q-eval.jsonl',\n",
       " 'data/eval_collection/Snowflake50+Routing+Prune20+Rerank10/testset-50q-eval.jsonl',\n",
       " 'data/eval_collection/Snowflake100+Routing+VRSD+Prune20+Rerank10/testset-50q-eval.jsonl',\n",
       " 'data/eval_collection/Snowflake100+Routing+Prune20+Rerank10_v2/testset-50q-eval.jsonl',\n",
       " 'data/eval_collection/Snowflake100+Routing+Prune20+Rerank10/testset-50q-eval.jsonl',\n",
       " 'data/eval_collection/Snowflake100+Routing+Prune20+Rerank10+no_refine/testset-50q-eval.jsonl',\n",
       " 'data/eval_collection/Snowflake100+Q_Expand_no_prefix+Routing+Prune20+Rerank10/testset-50q-eval.jsonl',\n",
       " 'data/eval_collection/Snowflake100+Q_Expand+Routing+Prune20+Rerank10/testset-50q-eval.jsonl',\n",
       " 'data/eval_collection/Snowflake10+Routing/testset-50q-eval.jsonl']"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_jsonl_list = glob.glob(\"data/eval_collection/*/*50q-result.jsonl\")\n",
    "result_jsonl_list.sort(reverse=True)\n",
    "eval_jsonl_list = glob.glob(\"data/eval_collection/*/*50q-eval.jsonl\")\n",
    "eval_jsonl_list.sort(reverse=True)\n",
    "eval_jsonl_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k, v in (zip(result_jsonl_list,eval_jsonl_list)):\n",
    "    ex = pd.read_json(k, lines=True)\n",
    "    ev = pd.read_json(v, lines=True)\n",
    "    m = pd.concat([ex, ev], axis=1)\n",
    "    collection_name = k.split(\"/\")[-2]\n",
    "    cols = ['id', 'doc_id', 'question_category', 'question_category_desc',\n",
    "       'user_category', 'user_category_desc', 'question', 'gold', 'answer',\n",
    "       'idx', 'faithfulness', 'relevance',\n",
    "       'faithfulness_correct', 'relevance_correct']\n",
    "    m[cols].to_csv(f'data/eval_collection/{collection_name}/merge-for-eda.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>faithfulness_correct</th>\n",
       "      <th>relevance_correct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collection_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Snowflake10+Routing</th>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.836735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snowflake500+Routing+Prune100+Rerank20</th>\n",
       "      <td>0.645833</td>\n",
       "      <td>0.802083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snowflake100+Q_Expand_no_prefix+Routing+Prune20+Rerank10</th>\n",
       "      <td>0.673469</td>\n",
       "      <td>0.806122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snowflake100+Q_Expand+Routing+Prune20+Rerank10</th>\n",
       "      <td>0.673469</td>\n",
       "      <td>0.846939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snowflake50+Routing+Prune20+Rerank10</th>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.846939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snowflake100+Routing+VRSD+Prune20+Rerank10</th>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.836735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snowflake100+Routing+Prune20+Rerank10_v2</th>\n",
       "      <td>0.770833</td>\n",
       "      <td>0.822917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snowflake100+Routing+Prune20+Rerank10</th>\n",
       "      <td>0.795918</td>\n",
       "      <td>0.816327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snowflake100+Routing+Prune20+Rerank10+no_refine</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.875000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    faithfulness_correct  \\\n",
       "collection_name                                                            \n",
       "Snowflake10+Routing                                             0.571429   \n",
       "Snowflake500+Routing+Prune100+Rerank20                          0.645833   \n",
       "Snowflake100+Q_Expand_no_prefix+Routing+Prune20...              0.673469   \n",
       "Snowflake100+Q_Expand+Routing+Prune20+Rerank10                  0.673469   \n",
       "Snowflake50+Routing+Prune20+Rerank10                            0.714286   \n",
       "Snowflake100+Routing+VRSD+Prune20+Rerank10                      0.714286   \n",
       "Snowflake100+Routing+Prune20+Rerank10_v2                        0.770833   \n",
       "Snowflake100+Routing+Prune20+Rerank10                           0.795918   \n",
       "Snowflake100+Routing+Prune20+Rerank10+no_refine                 0.833333   \n",
       "\n",
       "                                                    relevance_correct  \n",
       "collection_name                                                        \n",
       "Snowflake10+Routing                                          0.836735  \n",
       "Snowflake500+Routing+Prune100+Rerank20                       0.802083  \n",
       "Snowflake100+Q_Expand_no_prefix+Routing+Prune20...           0.806122  \n",
       "Snowflake100+Q_Expand+Routing+Prune20+Rerank10               0.846939  \n",
       "Snowflake50+Routing+Prune20+Rerank10                         0.846939  \n",
       "Snowflake100+Routing+VRSD+Prune20+Rerank10                   0.836735  \n",
       "Snowflake100+Routing+Prune20+Rerank10_v2                     0.822917  \n",
       "Snowflake100+Routing+Prune20+Rerank10                        0.816327  \n",
       "Snowflake100+Routing+Prune20+Rerank10+no_refine              0.875000  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sumdf_list = []\n",
    "stat_df_list = []\n",
    "for jsonl in eval_jsonl_list:\n",
    "    df = pd.read_json(jsonl, lines=True)\n",
    "    # df = df.iloc[:27]\n",
    "    stat_df_list.append({\n",
    "        \"n_faithfulness_na\": df['faithfulness'].isna().sum(),\n",
    "        \"n_relevance_na\": df['relevance'].isna().sum(),\n",
    "        \"collection_name\": jsonl.split(\"/\")[-2],\n",
    "    })\n",
    "    \n",
    "    # df['relevance_correct'] = (df['relevance'] >= 1).astype(int)\n",
    "    sumdf = {\n",
    "        # 'faithfulness_correct': (df['faithfulness'].dropna() >= 1).mean(),\n",
    "        # 'relevance_correct': (df['relevance'].dropna() >= 1).mean(),\n",
    "        'faithfulness_correct': (df['faithfulness_correct']).mean(),\n",
    "        'relevance_correct': (df['relevance_correct']).mean(),\n",
    "        \"collection_name\": jsonl.split(\"/\")[-2]\n",
    "    }\n",
    "    sumdf_list.append(sumdf)\n",
    "sumdf_list = pd.DataFrame(sumdf_list).set_index(\"collection_name\")\n",
    "sumdf_list = sumdf_list.sort_values(\"faithfulness_correct\")\n",
    "\n",
    "stat_df_list = pd.DataFrame(stat_df_list)\n",
    "sumdf_list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_faithfulness_na</th>\n",
       "      <th>n_relevance_na</th>\n",
       "      <th>collection_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>Snowflake500+Routing+Prune100+Rerank20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Snowflake50+Routing+Prune20+Rerank10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>Snowflake100+Routing+VRSD+Prune20+Rerank10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Snowflake100+Routing+Prune20+Rerank10_v2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Snowflake100+Routing+Prune20+Rerank10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Snowflake100+Routing+Prune20+Rerank10+no_refine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Snowflake100+Q_Expand_no_prefix+Routing+Prune2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Snowflake100+Q_Expand+Routing+Prune20+Rerank10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>Snowflake10+Routing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   n_faithfulness_na  n_relevance_na  \\\n",
       "0                  8               0   \n",
       "1                  5               0   \n",
       "2                  7               0   \n",
       "3                  4               0   \n",
       "4                  2               0   \n",
       "5                  3               0   \n",
       "6                  5               0   \n",
       "7                  6               0   \n",
       "8                 11               0   \n",
       "\n",
       "                                     collection_name  \n",
       "0             Snowflake500+Routing+Prune100+Rerank20  \n",
       "1               Snowflake50+Routing+Prune20+Rerank10  \n",
       "2         Snowflake100+Routing+VRSD+Prune20+Rerank10  \n",
       "3           Snowflake100+Routing+Prune20+Rerank10_v2  \n",
       "4              Snowflake100+Routing+Prune20+Rerank10  \n",
       "5    Snowflake100+Routing+Prune20+Rerank10+no_refine  \n",
       "6  Snowflake100+Q_Expand_no_prefix+Routing+Prune2...  \n",
       "7     Snowflake100+Q_Expand+Routing+Prune20+Rerank10  \n",
       "8                                Snowflake10+Routing  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat_df_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1333c3980>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sumdf_list.T.plot.barh()\n",
    "for container in ax.containers:\n",
    "    ax.bar_label(container, fmt=\"%.2g\")\n",
    "    \n",
    "ax.set_xlim(0, 1)\n",
    "ax.legend(loc=\"lower center\", bbox_to_anchor=(0.5, -0.3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
